{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "讲师： 沈福利\n",
    "\n",
    "本章节目标\n",
    "\n",
    "* 使用 plt.plot绘制散点图\n",
    "* 使用plt.scatter 绘制散点图\n",
    "* 鸢尾花花型尺寸分析\n",
    "* 萼片（sepal）和花瓣（petal）的大小关系\n",
    "* 不同种类（species）鸢尾花萼片和花瓣的大小关系\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 散点图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "另一种常用的绘图类型是简单散点图，它是线图的相似。这些点不是由线段连接的点，而是分别用点、圆或其他形状表示。\n",
    "\n",
    "我们将首先在jupyter-notebook 中导入相关的包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用 ``plt.plot``绘制散点图\n",
    "\n",
    "在前面的一部分我们使用 ``plt.plot``/``ax.plot`` 绘制线性图\n",
    "\n",
    "结果表明，同样的函数也可以生成散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 30) # x 轴的数据\n",
    "y = np.sin(x) # y 轴的数据\n",
    "\n",
    "#(x,y)  数据点进行画图\n",
    "plt.plot(x, y, 'o', color='black');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "函数调用中的第三个参数是表示用于打印的符号类型的字符。正如可以指定“-”、“--”等选项来控制线条样式一样，标记样式也有自己的短字符串代码集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.RandomState(0)\n",
    "for marker in ['o', '.', ',', 'x', '+', 'v', '^', '<', '>', 's', 'd']:\n",
    "    plt.plot( rng.rand(5),rng.rand(5), marker, label=\"marker='{0}'\".format(marker))\n",
    "plt.legend() # 图例\n",
    "plt.xlim(0, 1.8); # x 轴刻度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "符代码可以与线条和颜色代码一起使用，以绘制点以及连接点的线条"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html\n",
    "\n",
    "\n",
    "#1. Markers\n",
    "#\n",
    "#'.'\tpoint marker\n",
    "#','\tpixel marker\n",
    "#'o'\tcircle marker\n",
    "#'v'\ttriangle_down marker\n",
    "#'^'\ttriangle_up marker\n",
    "#'<'\ttriangle_left marker\n",
    "#'>'\ttriangle_right marker\n",
    "#'1'\ttri_down marker\n",
    "#'2'\ttri_up marker\n",
    "#'3'\ttri_left marker\n",
    "#'4'\ttri_right marker\n",
    "#'s'\tsquare marker\n",
    "#'p'\tpentagon marker\n",
    "#'*'\tstar marker\n",
    "#'h'\thexagon1 marker\n",
    "#'H'\thexagon2 marker\n",
    "#'+'\tplus marker\n",
    "#'x'\tx marker\n",
    "#'D'\tdiamond marker\n",
    "#'d'\tthin_diamond marker\n",
    "#'|'\tvline marker\n",
    "#'_'\thline marker\n",
    "#\n",
    "#\n",
    "#2. Line Styles\n",
    "#'-'\tsolid line style\n",
    "#'--'\tdashed line style\n",
    "#'-.'\tdash-dot line style\n",
    "#':'\tdotted line style\n",
    "#\n",
    "#\n",
    "#3. Colors\n",
    "#\n",
    "#'b'\tblue\n",
    "#'g'\tgreen\n",
    "#'r'\tred\n",
    "#'c'\tcyan\n",
    "#'m'\tmagenta\n",
    "#'y'\tyellow\n",
    "#'k'\tblack\n",
    "#'w'\twhite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iXr8POAfoHNj0PiAbeBTIA44CV2mth97BMWzevC8xe/bncLmyTUdJmqam33Po0PdYvvyFjGq3ONns2Z9m9uxPm46RNJZlYdtBgsEusrJKTMdJmGiv3C8HcrXWawivjXrXsNdXABdprc8f+F87cBvwqNb6PGA7cG2soeNpcCpQtzszrtoHud15uN2F+P3O73sUI+voeIm6uo/R13fEdJSksu0gW7fWUF//ZdNREira4n4u8CSA1noLcObgCwNX9dXAg0qpjUqpTw4/BngCuCCmxHG2c+f72bfvZtMxkm769Pdwxhl/y7gRBOItvb37aW39O253sekoSWVZbiorr2f69PeYjpJQ0Q6FLAbaI74PKqWytNYBoAC4H/g+4AaeVUr9Y9gxncCIn4Pq6uqijPIWn883qeNt26anpxifzx3T+U2YbJuHC4W6sSw3lpUbh1SJE6/2ppPEt3k5U6Y8zd69hxN4jugk7+d8MV1d0NRk/ncqUW2Otrh3AEUR37sGCjtAD3DvYH+6UuoZwn3zg8f0Dvx3xIlNamomP768rq4uhuN/PunzmhRbm8N6e9/g5ZfPpLr6vpRfUi0e7U03iWyzbQexLHdC3jsWyfw5+/2ttLU9T2np5Uk532hiaXNtbe2or0XbLbMReDeAUmo18FrEa4uBF5VSbqVUNuHumG2RxwCXAC9Eec6E6e19w3QEo3Jz5zN79ucoLHT+yAEx1N69n+aVVy7MyOXnBh05cj87d77fsfccor1yfwy4UCm1CbCAq5RSNwP7tNZ/UkptALYAfuAXWuudSqk7gJ8rpa4BjgFXxjH/pPX07GPr1mqU+ikVFZ8c/wAHsiyLqqpvmo4hDCgoWEJW1vSMHhJbUbEOcLNt2zn09R3E45lLVdWdjhkGGlVx11qHgOuGbd4d8fp3gO8MO8YLpNxMVdnZ01i48G6mTbvIdBTjenv309fXwJQp55qOIpJk1qwbTEcwrq3t7xw8+E3HTpqWsQ8xZWdPY86cz8toEWD37qvYs2f4v9nCqXp6NLYdNB3DOKdPmpaRxb2nZw/Hj/+FUMhvOkpKWLToPpYufcJ0DJEEoVA/27atZu/ez5qOYpzTJ03LyOLe0PBTXn/9/QSDmbVg7miKis4gN3fkCaSE01gsXvwg5eVXmQ5i3GiTozll0rSMLO4LFnyD5ctfJDt7qukoKaOzcwd79txAKBQYf2eRtlyubGbO/CDFxWeOv7PDVVXdicuVP2SbkyZNy8ji7nLlUFx8lukYKcXnexOv91F6enaPv7NIS8FgL0ePPoTf32I6SkooK1uLUg+euFJ3u0scNWlaxhX3Awe+RUPDT03HSDnTp7+HRYvu4bXX3uv4tTQzVVvbs+zZs47Ozm2mo6SMsrK1rFlzgPLyTzJ37hcdU9ghw1Zism2blpanyMtbkPJPZCZbc/Nv2bv3RscOCxMwbdolrFy5nYKC001HSTmnnOK8C76MKu6WZbF8+XOEQn2mo6ScsYaFSXF3BsuyKCo6w3SMlBYItDtmGuCM6JbxejewefP8E90Nzc2/Nx0p5Th9WFimO378Cfbvv4VAQEaIjWb37qvYvt05D/I5vrh7vRvQeh19fQcA+0R3g/QnD+X0YWGZrqtrG01Nv824dQuiMX36ZVRUrMO2Q6ajxIXji7vTn0KLF6cPC8t08+atZ9WqPSk5E2SqKC39Z2bP/gyW5Yyy6IxWjEG6GyZm+LAwl6vAUcPCMtngzI8uV47hJKkvGPTR1va86Rhx4fjiLt0NEzc4LKy6+geceuoGKewOsWvXv7B372dMx0gLR47cx44d5+Pzpf/Fn+NHy1RV3cnu3Z/EtvtPbJPuhrHNmnWj6QgiTmzbJjd3PtnZM0xHSQszZ15JQcFScnLKTUeJmeOLe1nZWvr6Gjlw4BsEgx2Om7M5Ufz+4/T27qO4eJXpKCIGlmWxcOF3TcdIG7m5s8nNnW06Rlw4vrgDzJ37b8yd+2+mY6QVra+hs/NlVq8+mNELOqQrr3cD9fXrHbkIRaL19zfj9f6SsrK15OTMNB1n0hxf3Lu7dxMMdlFUtFKKVBTmzbsVyNwl2NLZ4PBfedp4cvr7j7J//814PJXMnPkh03EmLarirpRyAQ8QXvi6D7haa70v4vWbgA8PfPtXrfV/KKUs4DCwd2D7Zq31l2NOPkGHD99NU9OjnHPOMSzLk6zTpr2iohWmI4hJkqeNY1NQsJRVq+rJy1tgOkpMor1yvxzI1VqvGVgg+y7gfQBKqSpgLbCK8CXfC0qpx4AeYJvW+tL4xZ64qqpvMnPmh3G5pLBHq7t7Fy0tTzBnjnRppRMZ/hsby7LSvrBD9EMhzwWeBNBabwEiJ4U+BFystQ4OrLWaDfiAlcAspdSzSqm/KqVUHHJPWHb2dKZOfWcyT+kYra1/Z//+L+LzHTYdRURBhv/GLhDoZM+e62lu/m/TUSbNGnzAYSKUUg8Df9BaPzHw/UGgSmsdiNjHAr4LFGmtr1VKvR0o01r/Til1LnC31nrIZOq1tbV2fv7QpyOj4fP5yM3NPWl7b++fsSw3ubmXTPq9U9VobY6nUKgTCOJyTUnoeSYiGe1NNZNtc2/vn+noWA9ELiOZS3Hx18nLe2/c8iVCqvycbTvE8ePvJS/vCgoKPpnQc8XS5p6eHlauXDnizcRou2U6gKKI713DCnsu8AjQCQwur/4PIACgtX5RKTVLKWVprYf8q1JTUxNllLfU1dWNePz27dficuVSU3PzpN87VY3WZqfKtPZCLG2uoaFhJvX1t+D3N6fVaJlU+jnb9v6kTEUQS5tra2tHfS3a4r4RuBT47UCf+2uDLwxcsf8P8IzW+tsRx3wNOA58Rym1DDg4vLAnyhlnPIfffzwZp3Ksrq7XOHDg6yxadC8eT6XpOGKCKio+TkXFx03HSGuDhd227bQcaRdtcX8MuFAptQmwgKuUUjcD+wA38A7Ao5Qa7Af5MvCfwC+VUu8hfAX/iXgEnwjLcpGTU5qs0zmSZblob3+R3t69UtzTRHv7Fjo6NlJRcS1ZWYWm46S1nTs/hMuVR03Nz0xHiVpUxX3gRul1wzZHLro5WsfRe6I5TzzU1X2cKVPeQUVFYvvLnC4//1TWrDnimJnyMkFr69McOvRdKitlGolY5efX4HKZvwcwGY58iCkU6sPnewO//1TTUdJe+ONo+CNpun48zTTz599GZeUNuN3pWZRSyYIFt5uOMGmOvBxzuTwsX/5/zJlzi+kojtDX18DLLy+jqelXpqOICcrJkYnC4sW27bScJdKRxd22gwBylRknOTll5OYuwO0uNh1FjOPQoe+zZ8+NRDPEWYxt795PU1u78kRdSReO65bx+9vYurWaRYvupazsStNxHMGyXCxZkr4Pc2SS/v5G+voOy4VNHJWVXUlR0VnYdjCtVrJyXHEPhbqZPv0y8vIWm47iOKFQP4FAh3zkT2ELF35HrtrjrKTkHEpKzjEdI2qO65bxeGZxyik/pbj4zPF3FhNm2yFeeqma+vovmo4iRiHdkYlz9OjDvPhiKc8952Lz5vl4vRtMRxqXo67cQ6E++vubyM2dYzqK41iWi3nz1pObW2U6ihjFK69cSF7eYpT6sekojuL1bmDv3htPrOaWLlMoO+rKvbX172zZMpe2tv8zHcWRKivXMW3aBaZjiBHYtk1JybkUFS03HcVx6uvXD1mmE96aQjmVOerKvaBgCVVV35al4RLI5zuEz/cmU6acZzqKiGBZFgsWfN10DEdK1ymUHXXlnps7h7lzb5G52xNoz57rqav7V7lpl2J6e/fLzyRB0nUKZccU956efbS2PkMoFBh/ZzFpVVXfZNmyp+SmXQoJBnt4+eUl1NcnbYGzjFJVdScu19ApyV2ufKqq7jSUaGIcU9wbGh7m1VcvJhTqNh3F0QoLl5Kfn9T1VsS4LBYv/lFar/eZysrK1qLUg3g88wALj2ceSj2Y0jdTwUF97vPnf5Xp099DVlaJ6SiO19lZS0vLk8ybl9o3lDKF251HeblM75tIZWVrU76YD+eYK3e3u0Bu8iXJoUN388YbX02rMb9OZdtBvN5f4/e3mY6SEQKBLnbtWovX+6jpKONyRHH3+Z7g6NEH5YZSEni9Gzh27I+E10C3T4z5lQJvRkfHFurqPkJr69Omo2QEt7uA3t59+P3HTEcZlyO6ZXy+J2loaKeycp3pKI5XX7+eUKh3yLbBMb/p9rHVCYqL17B8+SYKCk43HSUjWJbFypUvmY4xIY4o7iUl91BdXWE6RkZI1zG/TmVZLkpK1piOkZFCoQAuV+qW0KiSKaVcwAPAMqAPuFprvS/i9WuAawkvp3eH1vrPSqkZwKNAHnAUuEpr3ROP8F7vBurr19PXd5D29vRZBDideTxz6es7MOJ2kVw9PXvxejcwa9b15OSUmY6TUV555WJycspTevm9aPvcLwdytdZrgC8Bdw2+oJQqBz4LnANcBHxLKeUBbgMe1VqfB2wnXPxj5vVuQOt1A4VG+n6TJV3H/DpRR8cmDhy4A9uWZzuSraTkXIqLzzIdY0zRFvdzgScBtNZbgMipF98GbNRa92mt2wkvmr008hjgCSAuk5OE+36HfgBIh/ke0t3JY37nsnjxT+QTkwHl5R/nnHOa8XhmmY6ScebPv5VZs1J7jdpoO4yKgfaI74NKqSytdWCE1zqBkmHbB7edpK6uLqogY/X9Rvte6cjn8xls5wqmTHmCQOAgbW1X09DQSmtrYrOYba8ZE29zY8KzJEs6/ZxtO0Ao1IjbPTum90lUm6Mt7h1AUcT3roHCPtJrRUBbxPbeiG0nqampiSpIW9vofb/Rvlc6qqurM97OUKiauro1VFYuYerUxGZJhfYm21htbmz8JS0tf0Wph3C7C5KcLHHS6ee8c+e/0NW1jVWr9sY0HUcsba6trR31tWi7ZTYC7wZQSq0GXot4bStwnlIqVylVAtQAr0ceA1wCvBDlOUckfb/muVxZnHbab5k69XzTUTJOIHAcn++Nk/4GRPJUVl7PwoXfJvzMR+qJ9sr9MeBCpdQmwAKuUkrdDOzTWv9JKXUf4eLtAtZrrX1KqTuAnw+MpDkGxGVh08E+3sHRMh6PjJYxJRDoJBTykZNTajpKxpg9+3PMnv050zEy2tSp7zQdYUxRFXetdQi4btjm3RGvPwQ8NOwYL3DxZAOOZXC+h3T6KOc0oVA/mzfPoaLiKhYtutt0HMcLD//9Cn19h+SCJgX09TXS0bGR0tIPmI5yEkdMPyDMcblyWLjwu8yc+WHTURzvreG/B5Hhv6mhsfERdu68gr6+BtNRTiLFXcSssvIaWf0qCWT4b+opL7+KM898lZycctNRTpK6z86KtNLdvQu/v4UpU841HcWxZOqH1OPxVODxpObUJ3LlLuJC62vYt09u8CVSui735nQ9PfvYv/8LBAKdpqMMIcVdxEV19Q9ZsuRPpmM42oIFdxAepPYWGf5rXn//UQ4fvpeurm2mowwh3TIiLoqKzjAdwfHKyj5CZ2ctTU2/wu9vktEyKaKk5BzOOecYWVnFpqMMIcVdxE1r67N0dr7M3Lm3mI7iSJblprr6bqqrZchpKrEsd8oVdpBuGRFHra3/y6FDdxEK9ZmO4ji2bdPY+F/093tNRxEj8PkO8tprl9HWFpcH8ONCiruIm7lzv8SaNYdxuTymozhOT49m9+6P0dz8R9NRxAiys6fT07MHv7/ZdJQTpFtGxE0qfjR1ivx8xZlnvobHU2k6ihiB213AqlW7x98xieTKXcRVS8vT7NhxAcGgz3QUR7Esi8LC08nOnmY6ihhHKJQai6dIcRdxZdshAoFW+vuPmI7iGD7fQfbtuxmfTx5WSmXBoI+tW0/l4MFvmY4CSHEXcTZ9+sWceWYteXkLTUdxjM7OWo4c+SGhUL/pKGIMbncuU6f+E/n5qTGJofS5i4Sw7SBgYVly/RCr0tJ/5pxzjpOVVWg6ihhHdfU9piOcIH95Iu46Ol5i06YKOjpeMh3FMaSwp4+Ghv/Hpk2zee45F5s3zzc2a6cUdxF3eXmKqVMvwOXKMx0l7TU2/hevvXYpgUD7+DsL48LTMn9q4J6T2WmZo+qWUUpdCtwGBIBHBhbniHz9DOB+IAhN73H8AAATlElEQVT0AR/TWnsHVmg6h/AC2QDv01rLb6tDZWdP4dRTHzUdwxFCoT4CgU7cbhlmmg7C0y8PXXZvcFrmZE8TMeHirpTKBu4GzgK6gY1Kqce11pFLr98LfEZrvUMpdS3wReBmYAVwkdb6WPyii1TX39+MZbll+F4MKiuvprLyatMxxASl0rTM0XTL1BBeK7VVa90PvAicN2yfD2utdwx8nQX4lFIuoBp4UCm1USn1yZhTi5TX39/Mpk0VNDQ8NP7OYkS23Yttp+biy2JkqTQtczTFvRiI7ErpBEoid9BaNwAopc4GPk34Sr+AcFfNRwmvpXqDUmppDJlFGsjJKaW6+j6mT7/MdJS01d7+FXbsON90DBGFqqo7cbnyh2wzNS3zuN0ySqk7gHOBpUDk8IcioG2E/T8ErAfeo7VuVkq5gXu11j0Drz8DLANejTyurq5usm3A5/PFdHw6So82v5OODoDYc6ZHe+PL5VpDKNSXUe1O/5/zCgoLb6er63uEQs24XOUUFt5ES8sKWlpGblei2jxucdda3won+tx3KaWmAV3A24HvRe6rlPoocC1wvta6ZWDzYuDXSqkVhD8pnAv8fPh5amomP/C/rq4upuPTUTq02bZtOju3AhbFxW+L6b3Sob3xVlf3LxnYZif8nGuAL0x471jaXFtbO+prE76hqrX2K6VuBp4iXKQf0VofUUqdSrgL5jPAfcBB4I9KKYDntdZfU0ptALYAfuAXWuudk2qJSDuvvnopoVAXoZBPFpeIQmfndulvT3Pd3XUcPnwPCxd+18ikelENhdRaPw48PmzbLuCGgW9HHBahtf4O8J3JBBTpq6npUYLBTmw7PInY4JhfQAr8GEKhAK+8ciFZWecC/206jpikQKCVpqZHKS//GCUl5yT9/PIQk0iY+vr1Jwr7oMExv2J0lmVx6qm/Jj//X01HETEoLl7N2Wc3GSnsIMVdJFAqjflNJ5blZtq0C8jOPtV0FBEDy3Lhdpt7SluKu0iYVBrzmy5s2+bw4ftlel+H6O9vZvv28/F6k//EthR3kTCpNOY3XXR372Tfvs/S1vas6SgiDrKzp+NyZWOi1MqUvyJhBm+a1tevp6/voIyWmYDCwtNZvfpNsrKm0dp62HQcESPLcrFs2f8aObdcuYuEKitby5o1b7Js2d/Iyipm6tQLTUdKebm588jKKjIdQ8SRbYfw+1vG3zGOpLiLpMjOLiUra1pKrQ6fanp6NFpfQ2/vm6ajiDjbvv08du/+eFLPKd0yIikKC5ewfPlzpmOktO7uXTQ1/Zb58283HUXEWUXFNUkfOSPFXSRVMNiLbQdlZaEIXu+GiPsSc2hre07uSzhMRcUnkn5O6ZYRSeP3H2fTpnKOHv2x6SgpI7xyzzr6+g4QXrnnoLGVe0Ri+f1ttLT8LWnnk+IukiY7ezpz5vwbU6YMXwYgc9XXrycU6hmyTZ7idaaDB+/ktdfejd9/0mS6CSHdMiKp5s+/zXSElCJP8WaOysobKC29gqyskvF3jgO5chdJ5/MdoL19o+kYKUGe4s0ceXkLKC5ehWVZSTmfFHeRdLt3fwKtZV1QGHyKd+goCnmK17l8vkO88cZX8ftbE34uKe4i6RYu/D5LlvzVdIyUMHPmR7CsPFyuAsDC45mHUg/KaBmH6u9v5MCBb9LRsTnh55I+d5F0RUXLTUdIGbYdYvHiH5CTU87Uqe80HUckWFHRmZx9dgM5OTMTfi65chdGdHbuYP/+L2DbIdNRjHK5sigr+4gU9gxhWVZSCjtEeeWulLoUuA0IEF5m76Fhr68gvFLT3oFNP9Ja/0Yp9TXgPQPHfV5rvTXm5CKtdXe/zpEjP6KiYh35+dWm4xgRCHTR3PwbSkv/ReaSySCBQCdaX8OMGZdRVnZlws4z4eI+sED23cBZQDewUSn1uNa6MWK3FcD3tdZ3RRy3AngHsAqYA/xh4D1EBistvYLS0vfjduePv7NDtbT8Fa2vJi9PMWXKuabjiCRxuwvx+d4kEEjsTdVortxrgH1a61YApdSLwHnA7yL2WRl+Sb2P8NX754Fzgae11jZwUCmVpZQq1VrLDFIZzO3ONR3BuNLSD7JixQKKis40HUUkkWVZrFy5JeHniaa4FwPtEd93AsNH428FHtZa1yql1gNfA9qA4yMcN6S419XVRRFlKJ/PF9Px6cgJbQ4Gj9DefisFBdfg8Zw95r5OaO/ICoHdI77i3DaPLtPabNt99PXZCWnzuMVdKXUH4avvpcBLES8VES7ckR7TWg9uewy4H/ifgX3HOo6ampqJpx6mrq4upuPTkRPaHAwuYMeOILNnz2DGjLHb4oT2Rjpy5Mf09x9h/vyvj/pQi9PaPBGZ1ObXX7+CYLCL3Ny7J93m2traUV8bd7SM1vpWrfX5QBmwSCk1TSmVA7wdGD5Y8yml1NsGvn4XUAtsBC5SSrmUUnMBl9b6WPTNEE7jdueycuXLzJjxPtNRkq67+xXa2zcl7WlFkXqysorp7NyK13samzfPj/tkcRPultFa+5VSNwNPEf5H4RGt9RGl1KnAp7XWNwDXAz9QSvUDjcA6rXWHUuoFwv8QuIAb49oCkfZsO0Qg0EF29hTTUZJm8eIfEQoFTMcQhni9G2hq+s2JSeP6+g6g9TqAuD3AFtVQSK3144SHOkZu2wXcMPD1NuCkzlOt9e3A7ZMNKZxt27ZV5OZWcdppvzEdJSlCIT8uVzYulzxDmKnGmg00XsVdHmISxpWXf4rS0g+YjpEUwWAvW7bM48gRmdM+kyVjNlC5dBDGzZp1nekISRMMdjNjxuUUFJxmOoowyOOZO7BAy8nb40Wu3EVKCAQ6krpKjSk5OTNYvPgBWbAkw4VnAx36AF+8ZwOVK3eREnbtupKWlr8QnhlxLlVVdzpuZkS//zh+/zHy85XpKMKwwd/tt9bOjf/vvFy5C+O83g20tT0z8J19YuSA09YRbWh4hK1bT8HnO/njuMg8ZWVrWbPmTcrKdg78N74XM3LlLowLjxzoHbIt3iMHUkFZ2UfJySkjN3ee6SgiA8iVuzAuU9YR9XgqKC//mOkYIkNIcRfGZcI6oo2Nv8iIG8YidUhxF8YlY+SASbZtc+DAN2loeGj8nYWIE+lzF8aNNHJg/vzbHdPfblkWZ531SlIWRRZikBR3kRLKytaeKOZHj/6E+vovUlr6AcesUORyefB4yk3HEBlEumVEyiksXEFp6RWEQj7TUWLW39/Mtm3n0t6+yXQUkWGkuIuUU1x8FosX/5CcnFLTUWLi9W7gH/9YRkfHRnbuvMJx4/ZFapPiLlJWd3cd7e2JX44sEbzeDWi9jv7+BgD6+xsc+WCWSF1S3EVKsm2bnTs/wP79/246yqSMNaWrEMkgN1RFSrIsi5qaX6btWPdMeTBLpC65chcpq6hoBTk5M0zHmJRMeDBLpLaortyVUpcCtwEBwsvsPTTs9V8Dg+O95gNbtNYfVkr9CZgO+IFerfUlsQYXmaG3t559+z4PXA2kz8LJVVV3ovW6IV0zTnowS6S+CRd3pVQ2cDdwFtANbFRKPa61bhzcR2v94YF9pwLPAjcNvLQIOE1rbccruMgMbncxXV2vkpt7yHSUCWtt/Tt9fUeprn6AN9/8WsKmdBViLNFcudcA+7TWrQBKqReB84DfjbDvfwD3a60blFJlwBTgcaXUFOA/tdZ/jjG3yBA5OTNYvbqe3bu16SgTdvz4Xzh27L9529t2U1HxcdNxRIaKprgXA+0R33cCJcN3UkrNBN7FW1ftOcBdwL3ANMJX/Fu11k2Rx9XV1UURZSifzxfT8eko09rc29vL668/g9tdYTrKBFxDUdEH0Xp/TO+SaT9jkDbH07jFXSl1B3AusBR4KeKlIqBthEOuAB7VWgcHvm8Efqy1DgBNSqntgAKGFPeamsn3p9bV1cV0fDrKtDZv3XoNPT1/YM2aQ7jdBabjjMi2gwQCbWRnT4/L+2XazxikzdGqra0d9bVxR8torW/VWp8PlAGLlFLTlFI5wNuBzSMccgHwxLDvfwuglCoETgcy659mEbPc3ItYsOBOwG06yqi83g1s2VJFd/du01GEmPhQSK21H7gZeIpwUX9Ea31EKXWqUuqBiF0VUB9x3BPAXqXUFuBp4Cta62NxSS8yRnb2acyadT1ud67pKKMqKjqLysp1skaqSAlRDYXUWj8OPD5s2y7ghojvTxvhuM9PNqAQg2w7SHPzY2RnT2Xq1HeZjnOSgoIaFi78rukYQgDyEJNIM2+88WWOHv2x6RhDBIO97N//Jfr6GsffWYgkkeIu0oZluVm69H+ZPv0yNm+ez3PPudi8eb7xybja21/g8OG76O3dYzSHEJFkbhmRVjo6NrJnz3Unnvzs6zuA1usAjD0gNG3aP7F69Zt4PLOMnF+IkciVu0grqTbbYiDQASCFXaQcKe4iraTSbIt+/3G2bJnPkSM/Svq5hRiPFHeRVlJrtkUX5eWfoKTkPAPnFmJsUtxFWqmquhOXK3/INsvKNjLbYnb2VBYt+j6Fhacn/dxCjEeKu0grZWVrUepBPJ55gIXLlUdJyduTdjPV690wMFLHYtOmSuMjdYQYjYyWEWmnrGztiWIeCvXjcuUk5byD66IO3tAdXBd1MJMQqUSu3EVaGyzsPt9B9uy5gVCoP2HnSrWROkKMRYq7cISOjq14vRvo7t6ZsHOk0kgdIcYj3TLCEWbOvIIpU85P6JqrWVlTCARaT9ou66KKVCRX7sIxBgt7U9Nv6OzcHrf3te3w6pCLFt2PyzV0VkpZF1WkKinuwlGCwW727/93Dh36Xlzer6vrFXbseAf9/V7Ky9ei1MMnRup4PPNQ6kG5mSpSknTLCEdxuwtYtuxZcnPn4vVuoL5+fUwLVAeDvfT3NxEItJGTUzZkpI4QqUyKu3Cc/PxFA8MWryEU6gWin2Csr+8oHk8lJSWrOeus13G55E9FpBfplhGOFB622Dtk21jDFt96OMnFxo1lbN48l5aWpwGksIu0FPVvrVIqH/hf4FNa693DXpsBPArkAUeBq7TWPUqpa4BrgQBwh9b6zzEnF2IMow9bPBDx9VFycipoanp0yMNJfn8TlpWFz3coKVmFSISortyVUmcC/wcsHGWX24BHtdbnAduBa5VS5cBngXOAi4BvKaU8k48sxPhGG56YlRUeUeP3t7J58ywOH757xIeTbDvAgQPfSHhOIRIl2m4ZD/DPwGjLu58LPDnw9RPABcDbgI1a6z6tdTuwD1g6iaxCTNhIE4y5XPlUV98DgGW5qK7+IVOnXigPJwlHinaB7I0ASo26unsx0D7wdSdQMmxb5PYh6urqookyhM/ni+n4dJRpbY6+vSsoLLydrq67CYUacbnKKSy8iZaWFbS0DL7PO+noAJernFCo4aR3cLnKjf5/nGk/Y5A2x9O4xV0pdQfhK3KAd2mtg2Ps3gEUAb0D/22L2DZocPsQNTU1E4x8srq6upiOT0eZ1ubJtbcG+MK4e02b9t0hfe4QvspX6ruUlZn7/zjTfsYgbY5WbW3tqK+NW9y11rdGca6NwLuBnwGXAC8AW4E7lVK5hLt1aoDXo3hPIRJqcGhkrGPihUglMY/xUkpNAx7WWr8fuAP4+cDomGPAlVrrbqXUfYQLvQtYr7X2xXpeIeJJHk4STjOp4q61Pj/i6xbg/QNfe4GLR9j/IeChyUUUQggRLXmISQghHEiKuxBCOJAUdyGEcCAp7kII4UDW4EIEJtXW1poPIYQQaWjlypXWSNtTorgLIYSIL+mWEUIIB5LiLoQQDpS2qxAopVzAA8AyoA+4Wmu9z2yqxFJKZQOPAPMJT+Vwh9b6T0ZDJYlSaiZQC1w4fB0BJ1JKfRm4DMgBHtBa/9RwpIQa+N3+OeHf7SBwjZN/zkqpVcC3tdbnK6UWEZ6yxSY8NcuNWutQrOdI5yv3y4FcrfUa4EvAXYbzJMNHgeMD8+VfAvzAcJ6kGPjD/wnhCekcTyl1PnA24TUQ3gHMMRooOd4NZGmtzwa+DtxpOE/CKKVuAR4Gcgc2fR+4deDv2gLeF4/zpHNxPzF3vNZ6C3Cm2ThJ8TvgqxHfB0wFSbLvAT8mvLpXJrgIeA14DHgcyISVy/YAWQOfyIsBv+E8ibSfgSlbBqwEnh/4enAdjJilc3EfPk98UCmVtt1ME6G17tJadyqlioDfA9HM2JmWlFKfAJq11k+ZzpJEMwhfrHwQuA7YoJQacbibg3QR7pLZTXgeqvuMpkkgrfUfGPqPl6W1Hhy2OOJ6F5ORzsV9+DzxLq21469klVJzgGeB/9JaP2o6TxJ8ErhQKfUccAbwi4GlG53sOPCU1rpfa60BH1BqOFOi3US4zYsJ30f7+cA04Zkgsn99xPUuJiOdi/vg3PEopVYT/hjraEqpMuBp4Ita60dM50kGrfXbtdbvGJiJdAfwMa11o+FYifYicLFSylJKVQIFhAu+k7Xy1ifxFiAbcJuLk1TbB+6zwFvrYMQsnbsxHiN8RbeJ8E2IqwznSYavAFOBryqlBvveL9FaZ8SNxkyhtf6zUurthBe6cREePTHWCmhOcDfwiFLqBcIjhL6ite42nClZ/g14SCmVA9QR7nKNmTyhKoQQDpTO3TJCCCFGIcVdCCEcSIq7EEI4kBR3IYRwICnuQgjhQFLchRDCgaS4CyGEA0lxF0IIB/r/K0vejuagyK4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 30)\n",
    "y = np.sin(x)\n",
    "plt.plot(x, y, ':oy');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 30)\n",
    "y = np.sin(x)\n",
    "plt.plot(x, y, '-p', color='gray',\n",
    "         markersize=15, linewidth=4,\n",
    "         markerfacecolor='white',\n",
    "         markeredgecolor='gray',\n",
    "         markeredgewidth=2)\n",
    "plt.ylim(-1.2, 1.2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  使用``plt.scatter`` 绘制散点图\n",
    "下面我们使用 plt.scatter 函数来绘制散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 30)\n",
    "y = np.sin(x)\n",
    "plt.scatter(x, y, marker='*');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plt.scatter与plt.plot的主要区别在于，它可用于创建散点图，其中每个单独点的属性（大小、面颜色、边颜色等）可单独控制或映射到数据。\n",
    "为了更好地看到结果，使用alpha关键字来调整透明度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.RandomState(0)\n",
    "x = rng.randn(100)\n",
    "y = rng.randn(100)\n",
    "colors = rng.rand(100)\n",
    "sizes = 1000 * rng.rand(100)\n",
    "\n",
    "plt.scatter(x, y, c=colors, s=sizes, alpha=0.3,cmap='viridis')\n",
    "plt.colorbar();  # show color scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 鸢尾花花型尺寸分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 设置字体大小和图表的大小\n",
    "plt.rc('figure',figsize = (12,9))\n",
    "plt.rc('font',size = 15)\n",
    "# 中文乱码问题\n",
    "from matplotlib.font_manager import _rebuild\n",
    "_rebuild()\n",
    "plt.rcParams['font.sans-serif']=[u'SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们加载我们需要分析的数据，通过pandas 来处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = pd.read_csv('./data/iris.csv')\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.shape # 150 行  5列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      "sepal_length    150 non-null float64\n",
      "sepal_width     150 non-null float64\n",
      "petal_length    150 non-null float64\n",
      "petal_width     150 non-null float64\n",
      "species         150 non-null object\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 5.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# 查看字段、类型、非空的记录条数\n",
    "iris.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.057333      3.758000     1.199333\n",
       "std        0.828066     0.435866      1.765298     0.762238\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数值类特征分析\n",
    "iris.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(iris['species'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 萼片（sepal）和花瓣（petal）的大小关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. sepal 大小\n",
    "iris['sepal_size'] = iris['sepal_length'] * iris['sepal_width']\n",
    "\n",
    "# 2. petal 大小\n",
    "iris['petal_size'] = iris['petal_length'] * iris['petal_width']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>sepal_size</th>\n",
       "      <th>petal_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>17.85</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>14.70</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>15.04</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>14.26</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>18.00</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species  sepal_size  \\\n",
       "0           5.1          3.5           1.4          0.2  setosa       17.85   \n",
       "1           4.9          3.0           1.4          0.2  setosa       14.70   \n",
       "2           4.7          3.2           1.3          0.2  setosa       15.04   \n",
       "3           4.6          3.1           1.5          0.2  setosa       14.26   \n",
       "4           5.0          3.6           1.4          0.2  setosa       18.00   \n",
       "\n",
       "   petal_size  \n",
       "0        0.28  \n",
       "1        0.28  \n",
       "2        0.26  \n",
       "3        0.30  \n",
       "4        0.28  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绘制我们的散点图，需要x和y的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x =  0    17.85\n",
      "1    14.70\n",
      "Name: sepal_size, dtype: float64\n",
      "y =  0    0.28\n",
      "1    0.28\n",
      "Name: petal_size, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 数据\n",
    "x = iris['sepal_size']\n",
    "y = iris['petal_size']\n",
    "print('x = ',x[:2])\n",
    "print('y = ',y[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个绘图的对象\n",
    "fig,ax = plt.subplots()\n",
    "\n",
    "# 填充数据\n",
    "ax.scatter(x,y,s = 20,color = '#9932CC',alpha=0.75)\n",
    "# 添加标题和坐标的说明\n",
    "ax.set_title('萼片（sepal）和花瓣（petal）的大小关系')\n",
    "ax.set_xlabel('sepal_size')\n",
    "ax.set_ylabel('petal_size')\n",
    "\n",
    "\n",
    "# 去掉边框\n",
    "ax.spines['top'].set_color(None)\n",
    "ax.spines['right'].set_color(None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上述的散点图存在一个问题，不同类别的数据点我们是无法区分的。\n",
    "\n",
    "希望 不同类别（三个类别） 通过不同的颜色展示出来，这样的，我们就更好的区分出来"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 不同种类（species）鸢尾花萼片和花瓣的大小关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看类别的方法\n",
    "np.unique(iris['species'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11fc19320>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个绘制对象\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "\n",
    "# setosa 类别的数据\n",
    "x = iris[iris['species']=='setosa']['sepal_size']\n",
    "y = iris[iris['species']=='setosa']['petal_size']\n",
    "\n",
    "ax.scatter(x,y,s = 20,color = 'green',alpha=0.75)\n",
    "\n",
    "# versicolor 类别的数据\n",
    "x = iris[iris['species']=='versicolor']['sepal_size']\n",
    "y = iris[iris['species']=='versicolor']['petal_size']\n",
    "\n",
    "ax.scatter(x,y,s = 20,color = 'blue',alpha=0.75)\n",
    "\n",
    "\n",
    "# virginica 类别的数据\n",
    "x = iris[iris['species']=='virginica']['sepal_size']\n",
    "y = iris[iris['species']=='virginica']['petal_size']\n",
    "\n",
    "ax.scatter(x,y,s = 20,color = 'red',alpha=0.75)\n",
    "\n",
    "\n",
    "# 添加标题和坐标\n",
    "ax.set_title('不同种类（species）鸢尾花萼片和花瓣的大小关系')\n",
    "ax.set_xlabel('sepal_size')\n",
    "ax.set_ylabel('petal_size')\n",
    "# 去掉边框\n",
    "ax.spines['top'].set_color(None)\n",
    "ax.spines['right'].set_color(None)\n",
    "\n",
    "# 显示图例的信息\n",
    "ax.legend(loc = 'upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过数据分析发现 不同的颜色区分出来了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method legend in module matplotlib.axes._axes:\n",
      "\n",
      "legend(*args, **kwargs) method of matplotlib.axes._subplots.AxesSubplot instance\n",
      "    Places a legend on the axes.\n",
      "    \n",
      "    Call signatures::\n",
      "    \n",
      "        legend()\n",
      "        legend(labels)\n",
      "        legend(handles, labels)\n",
      "    \n",
      "    The call signatures correspond to three different ways how to use\n",
      "    this method.\n",
      "    \n",
      "    **1. Automatic detection of elements to be shown in the legend**\n",
      "    \n",
      "    The elements to be added to the legend are automatically determined,\n",
      "    when you do not pass in any extra arguments.\n",
      "    \n",
      "    In this case, the labels are taken from the artist. You can specify\n",
      "    them either at artist creation or by calling the\n",
      "    :meth:`~.Artist.set_label` method on the artist::\n",
      "    \n",
      "        line, = ax.plot([1, 2, 3], label='Inline label')\n",
      "        ax.legend()\n",
      "    \n",
      "    or::\n",
      "    \n",
      "        line.set_label('Label via method')\n",
      "        line, = ax.plot([1, 2, 3])\n",
      "        ax.legend()\n",
      "    \n",
      "    Specific lines can be excluded from the automatic legend element\n",
      "    selection by defining a label starting with an underscore.\n",
      "    This is default for all artists, so calling `Axes.legend` without\n",
      "    any arguments and without setting the labels manually will result in\n",
      "    no legend being drawn.\n",
      "    \n",
      "    \n",
      "    **2. Labeling existing plot elements**\n",
      "    \n",
      "    To make a legend for lines which already exist on the axes\n",
      "    (via plot for instance), simply call this function with an iterable\n",
      "    of strings, one for each legend item. For example::\n",
      "    \n",
      "        ax.plot([1, 2, 3])\n",
      "        ax.legend(['A simple line'])\n",
      "    \n",
      "    Note: This way of using is discouraged, because the relation between\n",
      "    plot elements and labels is only implicit by their order and can\n",
      "    easily be mixed up.\n",
      "    \n",
      "    \n",
      "    **3. Explicitly defining the elements in the legend**\n",
      "    \n",
      "    For full control of which artists have a legend entry, it is possible\n",
      "    to pass an iterable of legend artists followed by an iterable of\n",
      "    legend labels respectively::\n",
      "    \n",
      "        legend((line1, line2, line3), ('label1', 'label2', 'label3'))\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    \n",
      "    handles : sequence of `.Artist`, optional\n",
      "        A list of Artists (lines, patches) to be added to the legend.\n",
      "        Use this together with *labels*, if you need full control on what\n",
      "        is shown in the legend and the automatic mechanism described above\n",
      "        is not sufficient.\n",
      "    \n",
      "        The length of handles and labels should be the same in this\n",
      "        case. If they are not, they are truncated to the smaller length.\n",
      "    \n",
      "    labels : sequence of strings, optional\n",
      "        A list of labels to show next to the artists.\n",
      "        Use this together with *handles*, if you need full control on what\n",
      "        is shown in the legend and the automatic mechanism described above\n",
      "        is not sufficient.\n",
      "    \n",
      "    Other Parameters\n",
      "    ----------------\n",
      "    \n",
      "    loc : int or string or pair of floats, default: 'upper right'\n",
      "        The location of the legend. Possible codes are:\n",
      "    \n",
      "            ===============   =============\n",
      "            Location String   Location Code\n",
      "            ===============   =============\n",
      "            'best'            0\n",
      "            'upper right'     1\n",
      "            'upper left'      2\n",
      "            'lower left'      3\n",
      "            'lower right'     4\n",
      "            'right'           5\n",
      "            'center left'     6\n",
      "            'center right'    7\n",
      "            'lower center'    8\n",
      "            'upper center'    9\n",
      "            'center'          10\n",
      "            ===============   =============\n",
      "    \n",
      "    \n",
      "        Alternatively can be a 2-tuple giving ``x, y`` of the lower-left\n",
      "        corner of the legend in axes coordinates (in which case\n",
      "        ``bbox_to_anchor`` will be ignored).\n",
      "    \n",
      "    bbox_to_anchor : `.BboxBase` or pair of floats\n",
      "        Specify any arbitrary location for the legend in `bbox_transform`\n",
      "        coordinates (default Axes coordinates).\n",
      "    \n",
      "        For example, to put the legend's upper right hand corner in the\n",
      "        center of the axes the following keywords can be used::\n",
      "    \n",
      "           loc='upper right', bbox_to_anchor=(0.5, 0.5)\n",
      "    \n",
      "    ncol : integer\n",
      "        The number of columns that the legend has. Default is 1.\n",
      "    \n",
      "    prop : None or :class:`matplotlib.font_manager.FontProperties` or dict\n",
      "        The font properties of the legend. If None (default), the current\n",
      "        :data:`matplotlib.rcParams` will be used.\n",
      "    \n",
      "    fontsize : int or float or {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'}\n",
      "        Controls the font size of the legend. If the value is numeric the\n",
      "        size will be the absolute font size in points. String values are\n",
      "        relative to the current default font size. This argument is only\n",
      "        used if `prop` is not specified.\n",
      "    \n",
      "    numpoints : None or int\n",
      "        The number of marker points in the legend when creating a legend\n",
      "        entry for a `.Line2D` (line).\n",
      "        Default is ``None``, which will take the value from\n",
      "        :rc:`legend.numpoints`.\n",
      "    \n",
      "    scatterpoints : None or int\n",
      "        The number of marker points in the legend when creating\n",
      "        a legend entry for a `.PathCollection` (scatter plot).\n",
      "        Default is ``None``, which will take the value from\n",
      "        :rc:`legend.scatterpoints`.\n",
      "    \n",
      "    scatteryoffsets : iterable of floats\n",
      "        The vertical offset (relative to the font size) for the markers\n",
      "        created for a scatter plot legend entry. 0.0 is at the base the\n",
      "        legend text, and 1.0 is at the top. To draw all markers at the\n",
      "        same height, set to ``[0.5]``. Default is ``[0.375, 0.5, 0.3125]``.\n",
      "    \n",
      "    markerscale : None or int or float\n",
      "        The relative size of legend markers compared with the originally\n",
      "        drawn ones.\n",
      "        Default is ``None``, which will take the value from\n",
      "        :rc:`legend.markerscale`.\n",
      "    \n",
      "    markerfirst : bool\n",
      "        If *True*, legend marker is placed to the left of the legend label.\n",
      "        If *False*, legend marker is placed to the right of the legend\n",
      "        label.\n",
      "        Default is *True*.\n",
      "    \n",
      "    frameon : None or bool\n",
      "        Control whether the legend should be drawn on a patch\n",
      "        (frame).\n",
      "        Default is ``None``, which will take the value from\n",
      "        :rc:`legend.frameon`.\n",
      "    \n",
      "    fancybox : None or bool\n",
      "        Control whether round edges should be enabled around the\n",
      "        :class:`~matplotlib.patches.FancyBboxPatch` which makes up the\n",
      "        legend's background.\n",
      "        Default is ``None``, which will take the value from\n",
      "        :rc:`legend.fancybox`.\n",
      "    \n",
      "    shadow : None or bool\n",
      "        Control whether to draw a shadow behind the legend.\n",
      "        Default is ``None``, which will take the value from\n",
      "        :rc:`legend.shadow`.\n",
      "    \n",
      "    framealpha : None or float\n",
      "        Control the alpha transparency of the legend's background.\n",
      "        Default is ``None``, which will take the value from\n",
      "        :rc:`legend.framealpha`.  If shadow is activated and\n",
      "        *framealpha* is ``None``, the default value is ignored.\n",
      "    \n",
      "    facecolor : None or \"inherit\" or a color spec\n",
      "        Control the legend's background color.\n",
      "        Default is ``None``, which will take the value from\n",
      "        :rc:`legend.facecolor`.  If ``\"inherit\"``, it will take\n",
      "        :rc:`axes.facecolor`.\n",
      "    \n",
      "    edgecolor : None or \"inherit\" or a color spec\n",
      "        Control the legend's background patch edge color.\n",
      "        Default is ``None``, which will take the value from\n",
      "        :rc:`legend.edgecolor` If ``\"inherit\"``, it will take\n",
      "        :rc:`axes.edgecolor`.\n",
      "    \n",
      "    mode : {\"expand\", None}\n",
      "        If `mode` is set to ``\"expand\"`` the legend will be horizontally\n",
      "        expanded to fill the axes area (or `bbox_to_anchor` if defines\n",
      "        the legend's size).\n",
      "    \n",
      "    bbox_transform : None or :class:`matplotlib.transforms.Transform`\n",
      "        The transform for the bounding box (`bbox_to_anchor`). For a value\n",
      "        of ``None`` (default) the Axes'\n",
      "        :data:`~matplotlib.axes.Axes.transAxes` transform will be used.\n",
      "    \n",
      "    title : str or None\n",
      "        The legend's title. Default is no title (``None``).\n",
      "    \n",
      "    borderpad : float or None\n",
      "        The fractional whitespace inside the legend border.\n",
      "        Measured in font-size units.\n",
      "        Default is ``None``, which will take the value from\n",
      "        :rc:`legend.borderpad`.\n",
      "    \n",
      "    labelspacing : float or None\n",
      "        The vertical space between the legend entries.\n",
      "        Measured in font-size units.\n",
      "        Default is ``None``, which will take the value from\n",
      "        :rc:`legend.labelspacing`.\n",
      "    \n",
      "    handlelength : float or None\n",
      "        The length of the legend handles.\n",
      "        Measured in font-size units.\n",
      "        Default is ``None``, which will take the value from\n",
      "        :rc:`legend.handlelength`.\n",
      "    \n",
      "    handletextpad : float or None\n",
      "        The pad between the legend handle and text.\n",
      "        Measured in font-size units.\n",
      "        Default is ``None``, which will take the value from\n",
      "        :rc:`legend.handletextpad`.\n",
      "    \n",
      "    borderaxespad : float or None\n",
      "        The pad between the axes and legend border.\n",
      "        Measured in font-size units.\n",
      "        Default is ``None``, which will take the value from\n",
      "        :rc:`legend.borderaxespad`.\n",
      "    \n",
      "    columnspacing : float or None\n",
      "        The spacing between columns.\n",
      "        Measured in font-size units.\n",
      "        Default is ``None``, which will take the value from\n",
      "        :rc:`legend.columnspacing`.\n",
      "    \n",
      "    handler_map : dict or None\n",
      "        The custom dictionary mapping instances or types to a legend\n",
      "        handler. This `handler_map` updates the default handler map\n",
      "        found at :func:`matplotlib.legend.Legend.get_legend_handler_map`.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    \n",
      "    :class:`matplotlib.legend.Legend` instance\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    \n",
      "    Not all kinds of artist are supported by the legend command. See\n",
      "    :doc:`/tutorials/intermediate/legend_guide` for details.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    \n",
      "    .. plot:: gallery/api/legend.py\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ax.legend)"
   ]
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